Overview

Dataset statistics

Number of variables13
Number of observations2214
Missing cells1399
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory225.0 KiB
Average record size in memory104.1 B

Variable types

Numeric1
Text12

Alerts

2011 has 127 (5.7%) missing valuesMissing
2012 has 135 (6.1%) missing valuesMissing
2013 has 140 (6.3%) missing valuesMissing
2014 has 140 (6.3%) missing valuesMissing
2015 has 148 (6.7%) missing valuesMissing
2016 has 148 (6.7%) missing valuesMissing
2017 has 150 (6.8%) missing valuesMissing
2018 has 152 (6.9%) missing valuesMissing
2019 has 152 (6.9%) missing valuesMissing
2021 has 51 (2.3%) missing valuesMissing
2022 has 55 (2.5%) missing valuesMissing
school_name has unique valuesUnique

Reproduction

Analysis started2023-08-18 11:55:27.655142
Analysis finished2023-08-18 11:55:28.823934
Duration1.17 second
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

school_code
Real number (ℝ)

Distinct2213
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4208.2639
Minimum1001
Maximum8917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2023-08-18T11:55:28.932442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1253.2
Q12356
median3869
Q35191
95-th percentile8508.4
Maximum8917
Range7916
Interquartile range (IQR)2835

Descriptive statistics

Standard deviation2352.5789
Coefficient of variation (CV)0.55903788
Kurtosis-0.65456972
Mean4208.2639
Median Absolute Deviation (MAD)1492
Skewness0.72073264
Sum9312888
Variance5534627.7
MonotonicityNot monotonic
2023-08-18T11:55:29.125584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
< 0.1%
1327 1
 
< 0.1%
2749 1
 
< 0.1%
5643 1
 
< 0.1%
5128 1
 
< 0.1%
2751 1
 
< 0.1%
2752 1
 
< 0.1%
2759 1
 
< 0.1%
8211 1
 
< 0.1%
3022 1
 
< 0.1%
Other values (2203) 2203
99.5%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1015 1
< 0.1%
1016 1
< 0.1%
1017 1
< 0.1%
1019 1
< 0.1%
ValueCountFrequency (%)
8917 1
< 0.1%
8916 1
< 0.1%
8915 1
< 0.1%
8913 1
< 0.1%
8911 1
< 0.1%
8910 1
< 0.1%
8909 1
< 0.1%
8908 1
< 0.1%
8907 1
< 0.1%
8906 1
< 0.1%

school_name
Text

UNIQUE 

Distinct2214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2023-08-18T11:55:29.365928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length53
Mean length23.822042
Min length11

Characters and Unicode

Total characters52742
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2214 ?
Unique (%)100.0%

Sample

1st rowAbbotsford Public School
2nd rowAberdeen Public School
3rd rowAbermain Public School
4th rowAdaminaby Public School
5th rowAdamstown Public School
ValueCountFrequency (%)
school 2160
28.3%
public 1631
21.4%
high 359
 
4.7%
park 64
 
0.8%
central 61
 
0.8%
north 58
 
0.8%
west 57
 
0.7%
south 50
 
0.7%
college 49
 
0.6%
east 41
 
0.5%
Other values (1760) 3091
40.6%
2023-08-18T11:55:29.872917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 6040
 
11.5%
l 5473
 
10.4%
5407
 
10.3%
c 4061
 
7.7%
i 3130
 
5.9%
h 3071
 
5.8%
S 2413
 
4.6%
a 2361
 
4.5%
u 2305
 
4.4%
e 2148
 
4.1%
Other values (45) 16333
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39701
75.3%
Uppercase Letter 7617
 
14.4%
Space Separator 5407
 
10.3%
Dash Punctuation 9
 
< 0.1%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 6040
15.2%
l 5473
13.8%
c 4061
10.2%
i 3130
7.9%
h 3071
7.7%
a 2361
 
5.9%
u 2305
 
5.8%
e 2148
 
5.4%
b 1995
 
5.0%
r 1889
 
4.8%
Other values (16) 7228
18.2%
Uppercase Letter
ValueCountFrequency (%)
S 2413
31.7%
P 1839
24.1%
H 578
 
7.6%
C 465
 
6.1%
B 347
 
4.6%
W 259
 
3.4%
M 235
 
3.1%
G 188
 
2.5%
T 166
 
2.2%
N 146
 
1.9%
Other values (15) 981
12.9%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
' 1
 
12.5%
Space Separator
ValueCountFrequency (%)
5407
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47318
89.7%
Common 5424
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 6040
12.8%
l 5473
 
11.6%
c 4061
 
8.6%
i 3130
 
6.6%
h 3071
 
6.5%
S 2413
 
5.1%
a 2361
 
5.0%
u 2305
 
4.9%
e 2148
 
4.5%
b 1995
 
4.2%
Other values (41) 14321
30.3%
Common
ValueCountFrequency (%)
5407
99.7%
- 9
 
0.2%
, 7
 
0.1%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 6040
 
11.5%
l 5473
 
10.4%
5407
 
10.3%
c 4061
 
7.7%
i 3130
 
5.9%
h 3071
 
5.8%
S 2413
 
4.6%
a 2361
 
4.5%
u 2305
 
4.4%
e 2148
 
4.1%
Other values (45) 16333
31.0%

2011
Text

MISSING 

Distinct174
Distinct (%)8.3%
Missing127
Missing (%)5.7%
Memory size17.4 KiB
2023-08-18T11:55:30.163214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7546718
Min length2

Characters and Unicode

Total characters7836
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)1.6%

Sample

1st row94.3
2nd row93.6
3rd row93.5
4th row95.4
5th row94.8
ValueCountFrequency (%)
94.9 50
 
2.4%
94.6 47
 
2.3%
94.2 44
 
2.1%
93.3 43
 
2.1%
95.4 43
 
2.1%
95.1 42
 
2.0%
94 42
 
2.0%
94.5 42
 
2.0%
93 42
 
2.0%
95 41
 
2.0%
Other values (164) 1651
79.1%
2023-08-18T11:55:30.649409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2018
25.8%
. 1831
23.4%
4 620
 
7.9%
8 587
 
7.5%
3 560
 
7.1%
5 553
 
7.1%
2 483
 
6.2%
6 388
 
5.0%
1 344
 
4.4%
7 292
 
3.7%
Other values (5) 160
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5953
76.0%
Other Punctuation 1831
 
23.4%
Lowercase Letter 52
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2018
33.9%
4 620
 
10.4%
8 587
 
9.9%
3 560
 
9.4%
5 553
 
9.3%
2 483
 
8.1%
6 388
 
6.5%
1 344
 
5.8%
7 292
 
4.9%
0 108
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
s 14
26.9%
p 14
26.9%
n 12
23.1%
a 12
23.1%
Other Punctuation
ValueCountFrequency (%)
. 1831
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7784
99.3%
Latin 52
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2018
25.9%
. 1831
23.5%
4 620
 
8.0%
8 587
 
7.5%
3 560
 
7.2%
5 553
 
7.1%
2 483
 
6.2%
6 388
 
5.0%
1 344
 
4.4%
7 292
 
3.8%
Latin
ValueCountFrequency (%)
s 14
26.9%
p 14
26.9%
n 12
23.1%
a 12
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2018
25.8%
. 1831
23.4%
4 620
 
7.9%
8 587
 
7.5%
3 560
 
7.1%
5 553
 
7.1%
2 483
 
6.2%
6 388
 
5.0%
1 344
 
4.4%
7 292
 
3.7%
Other values (5) 160
 
2.0%

2012
Text

MISSING 

Distinct172
Distinct (%)8.3%
Missing135
Missing (%)6.1%
Memory size17.4 KiB
2023-08-18T11:55:30.952276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7662338
Min length2

Characters and Unicode

Total characters7830
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.3%

Sample

1st row95.9
2nd row94.2
3rd row93
4th row96.4
5th row95.4
ValueCountFrequency (%)
94.7 52
 
2.5%
94.5 52
 
2.5%
94.8 51
 
2.5%
93.5 48
 
2.3%
94 46
 
2.2%
94.1 45
 
2.2%
93.8 43
 
2.1%
95.5 41
 
2.0%
94.3 40
 
1.9%
94.6 38
 
1.8%
Other values (162) 1623
78.1%
2023-08-18T11:55:31.444208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1977
25.2%
. 1836
23.4%
4 662
 
8.5%
8 586
 
7.5%
5 564
 
7.2%
3 543
 
6.9%
2 434
 
5.5%
6 380
 
4.9%
1 338
 
4.3%
7 331
 
4.2%
Other values (5) 179
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5936
75.8%
Other Punctuation 1836
 
23.4%
Lowercase Letter 58
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1977
33.3%
4 662
 
11.2%
8 586
 
9.9%
5 564
 
9.5%
3 543
 
9.1%
2 434
 
7.3%
6 380
 
6.4%
1 338
 
5.7%
7 331
 
5.6%
0 121
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
s 18
31.0%
p 18
31.0%
n 11
19.0%
a 11
19.0%
Other Punctuation
ValueCountFrequency (%)
. 1836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7772
99.3%
Latin 58
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1977
25.4%
. 1836
23.6%
4 662
 
8.5%
8 586
 
7.5%
5 564
 
7.3%
3 543
 
7.0%
2 434
 
5.6%
6 380
 
4.9%
1 338
 
4.3%
7 331
 
4.3%
Latin
ValueCountFrequency (%)
s 18
31.0%
p 18
31.0%
n 11
19.0%
a 11
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1977
25.2%
. 1836
23.4%
4 662
 
8.5%
8 586
 
7.5%
5 564
 
7.2%
3 543
 
6.9%
2 434
 
5.5%
6 380
 
4.9%
1 338
 
4.3%
7 331
 
4.2%
Other values (5) 179
 
2.3%

2013
Text

MISSING 

Distinct173
Distinct (%)8.3%
Missing140
Missing (%)6.3%
Memory size17.4 KiB
2023-08-18T11:55:31.870963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7666345
Min length2

Characters and Unicode

Total characters7812
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)1.6%

Sample

1st row95.7
2nd row95.2
3rd row92
4th row97.2
5th row94.7
ValueCountFrequency (%)
95 52
 
2.5%
94.6 48
 
2.3%
94.9 46
 
2.2%
95.2 44
 
2.1%
95.1 44
 
2.1%
95.6 43
 
2.1%
94 43
 
2.1%
94.8 42
 
2.0%
94.4 40
 
1.9%
94.3 40
 
1.9%
Other values (163) 1632
78.7%
2023-08-18T11:55:32.350896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2033
26.0%
. 1832
23.5%
5 625
 
8.0%
4 619
 
7.9%
8 539
 
6.9%
3 524
 
6.7%
6 482
 
6.2%
2 409
 
5.2%
1 333
 
4.3%
7 268
 
3.4%
Other values (5) 148
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5920
75.8%
Other Punctuation 1832
 
23.5%
Lowercase Letter 60
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2033
34.3%
5 625
 
10.6%
4 619
 
10.5%
8 539
 
9.1%
3 524
 
8.9%
6 482
 
8.1%
2 409
 
6.9%
1 333
 
5.6%
7 268
 
4.5%
0 88
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
s 19
31.7%
p 19
31.7%
n 11
18.3%
a 11
18.3%
Other Punctuation
ValueCountFrequency (%)
. 1832
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7752
99.2%
Latin 60
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2033
26.2%
. 1832
23.6%
5 625
 
8.1%
4 619
 
8.0%
8 539
 
7.0%
3 524
 
6.8%
6 482
 
6.2%
2 409
 
5.3%
1 333
 
4.3%
7 268
 
3.5%
Latin
ValueCountFrequency (%)
s 19
31.7%
p 19
31.7%
n 11
18.3%
a 11
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2033
26.0%
. 1832
23.5%
5 625
 
8.0%
4 619
 
7.9%
8 539
 
6.9%
3 524
 
6.7%
6 482
 
6.2%
2 409
 
5.2%
1 333
 
4.3%
7 268
 
3.4%
Other values (5) 148
 
1.9%

2014
Text

MISSING 

Distinct175
Distinct (%)8.4%
Missing140
Missing (%)6.3%
Memory size17.4 KiB
2023-08-18T11:55:32.636349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7666345
Min length2

Characters and Unicode

Total characters7812
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)2.2%

Sample

1st row95.3
2nd row95.3
3rd row92.8
4th row96.2
5th row95.8
ValueCountFrequency (%)
94.2 56
 
2.7%
94.9 48
 
2.3%
95.1 48
 
2.3%
95 47
 
2.3%
95.7 42
 
2.0%
93.9 42
 
2.0%
95.3 41
 
2.0%
95.9 41
 
2.0%
94.7 41
 
2.0%
94.6 40
 
1.9%
Other values (165) 1628
78.5%
2023-08-18T11:55:33.109718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2060
26.4%
. 1832
23.5%
4 603
 
7.7%
5 595
 
7.6%
3 526
 
6.7%
8 523
 
6.7%
6 461
 
5.9%
2 390
 
5.0%
7 342
 
4.4%
1 319
 
4.1%
Other values (5) 161
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5908
75.6%
Other Punctuation 1832
 
23.5%
Lowercase Letter 72
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2060
34.9%
4 603
 
10.2%
5 595
 
10.1%
3 526
 
8.9%
8 523
 
8.9%
6 461
 
7.8%
2 390
 
6.6%
7 342
 
5.8%
1 319
 
5.4%
0 89
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
s 25
34.7%
p 25
34.7%
n 11
15.3%
a 11
15.3%
Other Punctuation
ValueCountFrequency (%)
. 1832
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7740
99.1%
Latin 72
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2060
26.6%
. 1832
23.7%
4 603
 
7.8%
5 595
 
7.7%
3 526
 
6.8%
8 523
 
6.8%
6 461
 
6.0%
2 390
 
5.0%
7 342
 
4.4%
1 319
 
4.1%
Latin
ValueCountFrequency (%)
s 25
34.7%
p 25
34.7%
n 11
15.3%
a 11
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2060
26.4%
. 1832
23.5%
4 603
 
7.7%
5 595
 
7.6%
3 526
 
6.7%
8 523
 
6.7%
6 461
 
5.9%
2 390
 
5.0%
7 342
 
4.4%
1 319
 
4.1%
Other values (5) 161
 
2.1%

2015
Text

MISSING 

Distinct173
Distinct (%)8.4%
Missing148
Missing (%)6.7%
Memory size17.4 KiB
2023-08-18T11:55:33.393936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8073572
Min length2

Characters and Unicode

Total characters7866
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)1.8%

Sample

1st row94.6
2nd row95
3rd row91.4
4th row90.9
5th row95.1
ValueCountFrequency (%)
94.3 47
 
2.3%
93.8 45
 
2.2%
94.8 45
 
2.2%
94.5 43
 
2.1%
95.1 41
 
2.0%
92.8 39
 
1.9%
94.4 39
 
1.9%
93.1 39
 
1.9%
94.1 39
 
1.9%
95.3 37
 
1.8%
Other values (163) 1652
80.0%
2023-08-18T11:55:33.869108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2002
25.5%
. 1867
23.7%
8 629
 
8.0%
4 591
 
7.5%
3 578
 
7.3%
5 542
 
6.9%
2 497
 
6.3%
1 372
 
4.7%
6 350
 
4.4%
7 279
 
3.5%
Other values (5) 159
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5951
75.7%
Other Punctuation 1867
 
23.7%
Lowercase Letter 48
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2002
33.6%
8 629
 
10.6%
4 591
 
9.9%
3 578
 
9.7%
5 542
 
9.1%
2 497
 
8.4%
1 372
 
6.3%
6 350
 
5.9%
7 279
 
4.7%
0 111
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
s 13
27.1%
p 13
27.1%
n 11
22.9%
a 11
22.9%
Other Punctuation
ValueCountFrequency (%)
. 1867
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7818
99.4%
Latin 48
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2002
25.6%
. 1867
23.9%
8 629
 
8.0%
4 591
 
7.6%
3 578
 
7.4%
5 542
 
6.9%
2 497
 
6.4%
1 372
 
4.8%
6 350
 
4.5%
7 279
 
3.6%
Latin
ValueCountFrequency (%)
s 13
27.1%
p 13
27.1%
n 11
22.9%
a 11
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2002
25.5%
. 1867
23.7%
8 629
 
8.0%
4 591
 
7.5%
3 578
 
7.3%
5 542
 
6.9%
2 497
 
6.3%
1 372
 
4.7%
6 350
 
4.4%
7 279
 
3.5%
Other values (5) 159
 
2.0%

2016
Text

MISSING 

Distinct177
Distinct (%)8.6%
Missing148
Missing (%)6.7%
Memory size17.4 KiB
2023-08-18T11:55:34.107376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7938045
Min length2

Characters and Unicode

Total characters7838
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)2.0%

Sample

1st row95.2
2nd row92.7
3rd row91.5
4th row85.5
5th row93.2
ValueCountFrequency (%)
94.3 54
 
2.6%
93.2 48
 
2.3%
94.1 47
 
2.3%
94 44
 
2.1%
93.8 41
 
2.0%
94.2 41
 
2.0%
94.5 41
 
2.0%
93.6 40
 
1.9%
92.6 39
 
1.9%
94.4 38
 
1.8%
Other values (167) 1633
79.0%
2023-08-18T11:55:34.486898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1976
25.2%
. 1853
23.6%
4 610
 
7.8%
8 597
 
7.6%
3 582
 
7.4%
2 516
 
6.6%
5 495
 
6.3%
1 403
 
5.1%
6 379
 
4.8%
7 267
 
3.4%
Other values (5) 160
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5935
75.7%
Other Punctuation 1853
 
23.6%
Lowercase Letter 50
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1976
33.3%
4 610
 
10.3%
8 597
 
10.1%
3 582
 
9.8%
2 516
 
8.7%
5 495
 
8.3%
1 403
 
6.8%
6 379
 
6.4%
7 267
 
4.5%
0 110
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
s 13
26.0%
p 13
26.0%
n 12
24.0%
a 12
24.0%
Other Punctuation
ValueCountFrequency (%)
. 1853
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7788
99.4%
Latin 50
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1976
25.4%
. 1853
23.8%
4 610
 
7.8%
8 597
 
7.7%
3 582
 
7.5%
2 516
 
6.6%
5 495
 
6.4%
1 403
 
5.2%
6 379
 
4.9%
7 267
 
3.4%
Latin
ValueCountFrequency (%)
s 13
26.0%
p 13
26.0%
n 12
24.0%
a 12
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1976
25.2%
. 1853
23.6%
4 610
 
7.8%
8 597
 
7.6%
3 582
 
7.4%
2 516
 
6.6%
5 495
 
6.3%
1 403
 
5.1%
6 379
 
4.8%
7 267
 
3.4%
Other values (5) 160
 
2.0%

2017
Text

MISSING 

Distinct175
Distinct (%)8.5%
Missing150
Missing (%)6.8%
Memory size17.4 KiB
2023-08-18T11:55:34.761283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7839147
Min length2

Characters and Unicode

Total characters7810
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)1.6%

Sample

1st row94.1
2nd row91.9
3rd row92.8
4th row95.6
5th row93
ValueCountFrequency (%)
94.6 46
 
2.2%
93.9 44
 
2.1%
93.6 44
 
2.1%
93.7 44
 
2.1%
94.7 43
 
2.1%
92.8 43
 
2.1%
93.5 42
 
2.0%
94.1 41
 
2.0%
93.3 41
 
2.0%
94 39
 
1.9%
Other values (165) 1637
79.3%
2023-08-18T11:55:35.195412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1973
25.3%
. 1841
23.6%
8 638
 
8.2%
3 593
 
7.6%
4 588
 
7.5%
5 472
 
6.0%
2 459
 
5.9%
1 425
 
5.4%
6 354
 
4.5%
7 303
 
3.9%
Other values (5) 164
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5911
75.7%
Other Punctuation 1841
 
23.6%
Lowercase Letter 58
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1973
33.4%
8 638
 
10.8%
3 593
 
10.0%
4 588
 
9.9%
5 472
 
8.0%
2 459
 
7.8%
1 425
 
7.2%
6 354
 
6.0%
7 303
 
5.1%
0 106
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
s 18
31.0%
p 18
31.0%
n 11
19.0%
a 11
19.0%
Other Punctuation
ValueCountFrequency (%)
. 1841
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7752
99.3%
Latin 58
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1973
25.5%
. 1841
23.7%
8 638
 
8.2%
3 593
 
7.6%
4 588
 
7.6%
5 472
 
6.1%
2 459
 
5.9%
1 425
 
5.5%
6 354
 
4.6%
7 303
 
3.9%
Latin
ValueCountFrequency (%)
s 18
31.0%
p 18
31.0%
n 11
19.0%
a 11
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1973
25.3%
. 1841
23.6%
8 638
 
8.2%
3 593
 
7.6%
4 588
 
7.5%
5 472
 
6.0%
2 459
 
5.9%
1 425
 
5.4%
6 354
 
4.5%
7 303
 
3.9%
Other values (5) 164
 
2.1%

2018
Text

MISSING 

Distinct185
Distinct (%)9.0%
Missing152
Missing (%)6.9%
Memory size17.4 KiB
2023-08-18T11:55:35.463324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7895247
Min length2

Characters and Unicode

Total characters7814
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)1.5%

Sample

1st row94.9
2nd row91.3
3rd row91
4th row92.2
5th row92.6
ValueCountFrequency (%)
93.7 42
 
2.0%
92.7 42
 
2.0%
92.6 40
 
1.9%
92.8 38
 
1.8%
92.4 38
 
1.8%
94.2 37
 
1.8%
94 36
 
1.7%
93.4 36
 
1.7%
92.5 34
 
1.6%
93.1 34
 
1.6%
Other values (175) 1685
81.7%
2023-08-18T11:55:35.964300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1874
24.0%
. 1845
23.6%
8 729
 
9.3%
2 573
 
7.3%
3 534
 
6.8%
4 511
 
6.5%
1 456
 
5.8%
5 439
 
5.6%
7 329
 
4.2%
6 306
 
3.9%
Other values (5) 218
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5919
75.7%
Other Punctuation 1845
 
23.6%
Lowercase Letter 50
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1874
31.7%
8 729
 
12.3%
2 573
 
9.7%
3 534
 
9.0%
4 511
 
8.6%
1 456
 
7.7%
5 439
 
7.4%
7 329
 
5.6%
6 306
 
5.2%
0 168
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
s 14
28.0%
p 14
28.0%
n 11
22.0%
a 11
22.0%
Other Punctuation
ValueCountFrequency (%)
. 1845
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7764
99.4%
Latin 50
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1874
24.1%
. 1845
23.8%
8 729
 
9.4%
2 573
 
7.4%
3 534
 
6.9%
4 511
 
6.6%
1 456
 
5.9%
5 439
 
5.7%
7 329
 
4.2%
6 306
 
3.9%
Latin
ValueCountFrequency (%)
s 14
28.0%
p 14
28.0%
n 11
22.0%
a 11
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1874
24.0%
. 1845
23.6%
8 729
 
9.3%
2 573
 
7.3%
3 534
 
6.8%
4 511
 
6.5%
1 456
 
5.8%
5 439
 
5.6%
7 329
 
4.2%
6 306
 
3.9%
Other values (5) 218
 
2.8%

2019
Text

MISSING 

Distinct198
Distinct (%)9.6%
Missing152
Missing (%)6.9%
Memory size17.4 KiB
2023-08-18T11:55:36.263612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7681862
Min length2

Characters and Unicode

Total characters7770
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)2.2%

Sample

1st row94.3
2nd row89.5
3rd row90.3
4th row94
5th row92.7
ValueCountFrequency (%)
92.3 40
 
1.9%
92.1 39
 
1.9%
93 38
 
1.8%
91.9 37
 
1.8%
93.3 35
 
1.7%
93.4 35
 
1.7%
94.1 33
 
1.6%
92.8 33
 
1.6%
92.6 33
 
1.6%
92.2 33
 
1.6%
Other values (188) 1706
82.7%
2023-08-18T11:55:36.758118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1824
23.5%
. 1823
23.5%
8 849
10.9%
2 555
 
7.1%
3 554
 
7.1%
4 492
 
6.3%
1 477
 
6.1%
5 329
 
4.2%
7 315
 
4.1%
6 307
 
4.0%
Other values (5) 245
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5883
75.7%
Other Punctuation 1823
 
23.5%
Lowercase Letter 64
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1824
31.0%
8 849
14.4%
2 555
 
9.4%
3 554
 
9.4%
4 492
 
8.4%
1 477
 
8.1%
5 329
 
5.6%
7 315
 
5.4%
6 307
 
5.2%
0 181
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
s 21
32.8%
p 21
32.8%
n 11
17.2%
a 11
17.2%
Other Punctuation
ValueCountFrequency (%)
. 1823
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7706
99.2%
Latin 64
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1824
23.7%
. 1823
23.7%
8 849
11.0%
2 555
 
7.2%
3 554
 
7.2%
4 492
 
6.4%
1 477
 
6.2%
5 329
 
4.3%
7 315
 
4.1%
6 307
 
4.0%
Latin
ValueCountFrequency (%)
s 21
32.8%
p 21
32.8%
n 11
17.2%
a 11
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1824
23.5%
. 1823
23.5%
8 849
10.9%
2 555
 
7.1%
3 554
 
7.1%
4 492
 
6.3%
1 477
 
6.1%
5 329
 
4.2%
7 315
 
4.1%
6 307
 
4.0%
Other values (5) 245
 
3.2%

2021
Text

MISSING 

Distinct260
Distinct (%)12.0%
Missing51
Missing (%)2.3%
Memory size17.4 KiB
2023-08-18T11:55:37.089814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7494221
Min length2

Characters and Unicode

Total characters8110
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)3.1%

Sample

1st row94.3
2nd row88.4
3rd row89.2
4th row90.7
5th row93.3
ValueCountFrequency (%)
94.1 34
 
1.6%
92.1 34
 
1.6%
91.2 32
 
1.5%
91.1 32
 
1.5%
92 29
 
1.3%
92.2 28
 
1.3%
91.6 28
 
1.3%
91.5 27
 
1.2%
sp 27
 
1.2%
93 27
 
1.2%
Other values (250) 1865
86.2%
2023-08-18T11:55:37.613453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1892
23.3%
9 1652
20.4%
8 1055
13.0%
1 536
 
6.6%
2 528
 
6.5%
4 486
 
6.0%
3 462
 
5.7%
5 414
 
5.1%
7 413
 
5.1%
6 360
 
4.4%
Other values (5) 312
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6144
75.8%
Other Punctuation 1892
 
23.3%
Lowercase Letter 74
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1652
26.9%
8 1055
17.2%
1 536
 
8.7%
2 528
 
8.6%
4 486
 
7.9%
3 462
 
7.5%
5 414
 
6.7%
7 413
 
6.7%
6 360
 
5.9%
0 238
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
s 27
36.5%
p 27
36.5%
n 10
 
13.5%
a 10
 
13.5%
Other Punctuation
ValueCountFrequency (%)
. 1892
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8036
99.1%
Latin 74
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1892
23.5%
9 1652
20.6%
8 1055
13.1%
1 536
 
6.7%
2 528
 
6.6%
4 486
 
6.0%
3 462
 
5.7%
5 414
 
5.2%
7 413
 
5.1%
6 360
 
4.5%
Latin
ValueCountFrequency (%)
s 27
36.5%
p 27
36.5%
n 10
 
13.5%
a 10
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1892
23.3%
9 1652
20.4%
8 1055
13.0%
1 536
 
6.6%
2 528
 
6.5%
4 486
 
6.0%
3 462
 
5.7%
5 414
 
5.1%
7 413
 
5.1%
6 360
 
4.4%
Other values (5) 312
 
3.8%

2022
Text

MISSING 

Distinct280
Distinct (%)13.0%
Missing55
Missing (%)2.5%
Memory size17.4 KiB
2023-08-18T11:55:37.913839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.770264
Min length2

Characters and Unicode

Total characters8140
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)3.1%

Sample

1st row89
2nd row83.9
3rd row85.2
4th row75.7
5th row87.5
ValueCountFrequency (%)
86.4 29
 
1.3%
89 28
 
1.3%
88.4 27
 
1.3%
86.9 27
 
1.3%
87.1 25
 
1.2%
88.1 25
 
1.2%
88 23
 
1.1%
86.3 23
 
1.1%
85.6 23
 
1.1%
89.1 23
 
1.1%
Other values (270) 1906
88.3%
2023-08-18T11:55:38.349428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 1952
24.0%
. 1911
23.5%
7 753
 
9.3%
9 742
 
9.1%
6 495
 
6.1%
1 430
 
5.3%
5 419
 
5.1%
4 408
 
5.0%
3 394
 
4.8%
2 361
 
4.4%
Other values (5) 275
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6165
75.7%
Other Punctuation 1911
 
23.5%
Lowercase Letter 64
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 1952
31.7%
7 753
 
12.2%
9 742
 
12.0%
6 495
 
8.0%
1 430
 
7.0%
5 419
 
6.8%
4 408
 
6.6%
3 394
 
6.4%
2 361
 
5.9%
0 211
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
s 22
34.4%
p 22
34.4%
n 10
15.6%
a 10
15.6%
Other Punctuation
ValueCountFrequency (%)
. 1911
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8076
99.2%
Latin 64
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
8 1952
24.2%
. 1911
23.7%
7 753
 
9.3%
9 742
 
9.2%
6 495
 
6.1%
1 430
 
5.3%
5 419
 
5.2%
4 408
 
5.1%
3 394
 
4.9%
2 361
 
4.5%
Latin
ValueCountFrequency (%)
s 22
34.4%
p 22
34.4%
n 10
15.6%
a 10
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 1952
24.0%
. 1911
23.5%
7 753
 
9.3%
9 742
 
9.1%
6 495
 
6.1%
1 430
 
5.3%
5 419
 
5.1%
4 408
 
5.0%
3 394
 
4.8%
2 361
 
4.4%
Other values (5) 275
 
3.4%

Interactions

2023-08-18T11:55:27.988786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-08-18T11:55:28.195729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-18T11:55:28.446614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-18T11:55:28.670503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

school_codeschool_name20112012201320142015201620172018201920212022
01001.0Abbotsford Public School94.395.995.795.394.695.294.194.994.394.389
11002.0Aberdeen Public School93.694.295.295.39592.791.991.389.588.483.9
21003.0Abermain Public School93.5939292.891.491.592.89190.389.285.2
31007.0Adaminaby Public School95.496.497.296.290.985.595.692.29490.775.7
41008.0Adamstown Public School94.895.494.795.895.193.29392.692.793.387.5
51009.0Adelong Public School93.594.393.893.493.392.791.392.189.79088.3
63640.0Afterlee Public School87.589.892.493.493.9979394.295.190.6NaN
78493.0Airds High School81.280.482.783.482.38380.181.578.17872.5
85748.0Ajuga SchoolNaNNaNNaNNaNNaNNaNNaNNaNNaN76.774.8
94177.0Albert Park Public School89.688.685.988.387.187.98790.589.286.981
school_codeschool_name20112012201320142015201620172018201920212022
22043559.0Yetman Public School94.295.796.795.589.596.394.692.885.783.884.7
22053561.0Yoogali Public School93.993.995.394.393.894.394.193.592.994.886.5
22064482.0York Public School94.29495.594.193.894.594.293.492.392.886.7
22078155.0Young High School85.685.187.887.388.485.985.987.387.783.879.7
22084131.0Young North Public School90.790.691.19292.493.393.693.692.891.588
22093563.0Young Public School94.193.493.694.194.294.494.493.993.591.886.9
22104124.0Yowie Bay Public School96.495.79696.396.195.995.59593.894.789.7
22115460.0Yudi Gunyi SchoolNaNNaNNaNNaNNaNNaNNaNNaNNaN34.129.4
22123566.0Zig Zag Public School8990.693.691.892.793.591.290.78886.684
2213NaNNSW government92.492.392.993.192.492.492.491.691.190.185.2